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| 循环量化分析 (RQA)× | 样本熵× | |
|---|---|---|
| 领域 | 复杂系统 | 复杂系统 |
| 方法族 | Machine learning | Machine learning |
| 起源年份≠ | 2007 | 2000 |
| 提出者≠ | Marwan, Romano, Thiel & Kurths | Richman & Moorman |
| 类型≠ | Nonlinear time-series characterization | Nonlinear entropy measure |
| 开创性文献≠ | Marwan, N., Romano, M. C., Thiel, M., & Kurths, J. (2007). Recurrence plots for the analysis of complex systems. Physics Reports, 438(5–6), 237–329. DOI ↗ | Richman, J. S., & Moorman, J. R. (2000). Physiological time-series analysis using approximate entropy and sample entropy. American Journal of Physiology, 278(6), H2039–H2049. DOI ↗ |
| 别名 | RQA, Recurrence Plot Analysis, Nonlinear Recurrence Analysis, Tekrarlama Kantifikasyon Analizi | SampEn, Sample Entropy (SampEn), Örneklem Entropisi, Nonlinear Complexity Measure |
| 相关 | 2 | 2 |
| 摘要≠ | Recurrence Quantification Analysis (RQA) is a nonlinear method for characterizing the dynamics of a time series by quantifying the small-scale structure of its recurrence plot. Introduced in its modern, comprehensive form by Marwan, Romano, Thiel, and Kurths in 2007, RQA extracts scalar measures — such as recurrence rate, determinism, laminarity, and Shannon entropy — that capture periodicity, chaos, stationarity, and transitions in complex dynamical systems. | Sample Entropy (SampEn) is a nonlinear measure of the complexity and regularity of a time series. Introduced by Richman and Moorman in 2000 as an improvement over Approximate Entropy (ApEn), it quantifies the likelihood that similar patterns of a given length in the series remain similar when extended by one additional data point. A higher SampEn value indicates greater irregularity and complexity, while a lower value indicates more regularity or self-similarity. |
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